CN112683811A - Forest canopy spectrum quaternary phase change monitoring method based on medium-resolution image - Google Patents
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Abstract
The invention discloses a forest canopy spectrum quaternary phase change monitoring method based on medium-resolution images, which comprises the following steps of: dividing the same forest stand into a plurality of sub-regions with the same size, and randomly selecting a certain number of sub-regions as sample regions; acquiring remote sensing image data of a sample area for years as sample data; preprocessing sample data; extracting spectral information in the preprocessed sample data, and respectively calculating the normalized vegetation index of each image shooting date of the forest canopy in a plurality of years based on the spectral information; overlapping the normalized vegetation indexes of all image shooting dates in the forest canopy for a plurality of years to one year; introducing a logistic curve and improving the logistic curve; and fitting the normalized vegetation index superposed to one year by using the improved logistic curve to obtain a seasonal variation curve of the forest canopy spectrum. The invention can utilize time sequence data to carry out contrastive analysis, interpretation and inversion on different forest species, and utilizes the quaternary phase change curve to be beneficial to mastering the forest change rhythm.
Description
Technical Field
The invention relates to the technical field of remote sensing monitoring, in particular to a forest canopy spectrum quaternary change monitoring method based on a medium-resolution image.
Background
A forest shows a regular change in morphology and physiology each year with seasonal changes, which are regularly manifested to some extent by its spectral characteristics. The method is not only key for remote sensing interpretation, but also provides theoretical basis for tree species identification, dynamic monitoring and biochemical parameter inversion.
The traditional monitoring method mostly utilizes time sequence NDVI data of data sources such as MODIS, NOAA/AVHRR, SPOT-VGT and the like, but is mostly used for large-scale monitoring due to low spatial resolution (higher than 250m), and has great limitation on medium-scale and small-scale monitoring. The medium-resolution images such as Landsat (resolution 30m) lack time series data, a large number of images need to be processed for data acquisition, the required time is long, the data volume is small in one year, and the requirement of monitoring seasonal phase change is difficult to meet.
Disclosure of Invention
In view of the above, the invention provides a forest canopy spectrum quaternary phase change monitoring method based on a medium-resolution image, which can obtain time sequence data of the medium-resolution image, perform comparative analysis, interpretation and inversion on different forest varieties by using the time sequence data, and a fitted quaternary phase change curve is beneficial to knowing the relation between forest change characteristics and a ground spectrum and mastering forest change rhythm.
In order to achieve the purpose, the invention adopts the following technical scheme:
a forest canopy spectrum quaternary change monitoring method based on medium-resolution images comprises the following steps:
dividing the same forest stand into a plurality of sub-regions with the same size, and randomly selecting a certain number of the sub-regions as sample regions;
acquiring remote sensing image data of the sample area for years as sample data;
preprocessing the sample data;
extracting spectral information in the preprocessed sample data, and respectively calculating the normalized vegetation index of each image shooting date of the forest canopy in multiple years based on the spectral information;
overlapping the normalized vegetation indexes of all image shooting dates in the forest canopy for a plurality of years to one year;
introducing a logistic curve and improving the logistic curve;
and fitting the normalized vegetation index superposed to one year by using the improved logistic curve to obtain a seasonal variation curve of the forest canopy spectrum.
Preferably, the remote sensing image data is a medium-resolution image obtained based on a Landsat satellite, and the spatial resolution of the remote sensing image data is 30 m.
Preferably, the sample data is extracted and preprocessed by using a Google Earth Engine.
Preferably, the preprocessing the sample data includes:
selecting a remote sensing image with the best imaging quality in the same period in the sample data;
carrying out cloud layer detection on all the selected remote sensing images, and removing the remote sensing images covered by clouds and cloud images;
and cutting the remote sensing image subjected to cloud layer detection.
Preferably, the spectral information is reflectance values of a red band, a green band, a blue band, and a near-infrared band.
Preferably, the calculation formula of the normalized vegetation index is as follows:
in the above formula, ρNIRRepresenting the reflectivity, p, of the near infrared bandREDIndicating the reflectivity in the red band.
Preferably, a logistic curve is introduced and modified, comprising:
introducing the logistic growth curve, wherein the expression is as follows:
in the above formula, m is a slope, n is an offset, t is time, and y is spectral information;
the logistic growth curve is preliminarily improved to be suitable for describing blades with different shapes in forest canopies, and the expression is as follows:
in the above formula, VminBackground value, V, for spectral informationampIs a variation range;
introducing a logistic descending curve, and fitting the logistic descending curve with the logistic increasing curve to generate a final logistic curve equation, wherein the expression of the logistic descending curve is as follows:
in the above formula, m1For the slope of the rise period, m2For the falling phase slope, n1For the offset of the rise period, n2Is the decline period offset.
Preferably, the logistic curve is introduced and modified, and the method further comprises the following steps:
introducing a variation coefficient, fitting by using remote sensing image data for many years, and obtaining the influence of an annual effect on the growth initial period of forest vegetation, wherein the expression is as follows:
in the above formula, b1A coefficient of variation in the rise period, b2Is the coefficient of variation during the descent.
According to the technical scheme, compared with the prior art, the forest canopy spectrum quaternary phase change monitoring method based on the medium-resolution image is disclosed, remote sensing image data of the same forest stand for years are overlapped, sample data are enriched, the sample data form time sequence data, the defect that the traditional medium-resolution image lacks time sequence data is overcome, and the time sequence data can be used for further research on contrastive analysis, interpretation, inversion and the like of different forest varieties; the fitted quaternary change curve is beneficial to knowing the relation between the forest change characteristic and the ground spectrum and mastering the forest change rhythm.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a method for monitoring spectral quaternary changes of a forest canopy based on a medium-resolution image according to the present invention;
FIG. 2 is a diagram of normalized vegetation index seasonal variation of original forest of broadleaf red pine provided by the present invention;
FIG. 3 is the diagram of the variation of the normalized vegetation index of the secondary forest of broadleaf red pine.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention takes Changbai mountain broad-leaved pinus red forest as an example, and utilizes all Landsat satellite remote sensing image data (spatial resolution of 30m) in the period of 1984 and 2019 in Changbai mountain area to preprocess the remote sensing image data and remove images with poor quality. And extracting the spectral reflectance value of the canopy of the broad-leaved pinus sylvestris, calculating the vegetation index, and fitting the seasonal phase change curve by using a nonlinear curve.
As shown in fig. 1, the implementation process of the embodiment of the present invention specifically includes the following steps:
and S1, dividing the same forest stand into a plurality of sub-regions with the same size, and randomly selecting a certain number of sub-regions as sample regions.
In this embodiment, 50 sub-regions of 300 × 300m are randomly selected as the sample regions.
And S2, acquiring the remote sensing image data of the sample area for years as sample data.
And S3, preprocessing the sample data.
And preprocessing a large amount of remote sensing image data by using a Google Earth Engine.
Firstly, selecting a remote sensing image with the best imaging quality in the same period in sample data.
Secondly, cloud layer detection is carried out on all selected remote sensing images, and the remote sensing images covered by the cloud and the cloud shadow are removed.
The Landsat8 adopts a QA wave band to identify the cloud and the cloud shadow, and Landsat5 and Landsat 7 adopt an Fcast algorithm to identify the cloud.
QA band cloud identification refers to: cloud regions can be directly identified through the rolling cloud Band (Band9) of Landsat8, and cloud layer conditions can be determined according to a threshold value.
Fmak algorithm cloud identification: fmask is a method for extracting cloud and cloud shadow areas in remote sensing images, and mainly aims at TM/ETM + images of Landsat5 and 7.
And finally, cutting the remote sensing image subjected to cloud layer detection.
And S4, extracting spectral information in the preprocessed sample data, and respectively calculating the normalized vegetation index of each image shooting date of the forest canopy in years based on the spectral information.
Spectral information is mainly extracted as reflectance values of red, green, blue and near-infrared bands, and normalized vegetation index (NDVI) is calculated.
NDVI is the most commonly used normalized vegetation index, which may reflect the growth and coverage of vegetation. The calculation formula is as follows:
in the above formula, ρNIRRepresenting the reflectivity, p, of the near infrared bandREDIndicating the reflectivity in the red band.
And S5, overlapping the normalized vegetation indexes of all the image shooting dates in the forest canopy for a plurality of years to one year.
The traditional time sequence data has lower spatial resolution, and the medium and high resolution images lack time sequence data.
And S6, introducing a logistic curve and improving the logistic curve.
The fitting of the data adopts a logistic curve, the curve conforms to the growth characteristics of trees, the trees grow slowly in the juvenile period, grow rapidly in the middle-aged period, and grow gradually and stop after nearly mature. Assuming that a single leaf appears normally distributed over time and the total leaf coverage is a simple summation, its change over time must approximate a logistic curve.
One common logistic curve formula is:
in the above formula, m is a slope, n is an offset, t is time, and y is spectral information.
For a dense forest, starting from a non-vegetation background, the green coverage area must theoretically start from 0, and then appear as a cumulative function to 1, with a value generally less than 1 if a pixel is blended with non-vegetation. The logistic growth curve can explain most of the characteristics during the growing season, but the presence of coniferous trees, with its maximum and minimum values, tends to deviate from the ideal situation. Therefore, a certain improvement on the logistic is needed, and the improved formula is as follows:
in the above formula, VminBackground value, V, for spectral informationampIs the variation range.
For the data of the Changbai mountain, the data of the whole year is selected for research, and the data comprises two processes of tree leaf growth and withering, so that a logistic growth curve and a logistic descent curve are combined for fitting. The formula is as follows:
in the above formula, m1For the slope of the rise period, m2For the falling phase slope, n1For the offset of the rise period, n2Is the decline period offset.
And S7, fitting the normalized vegetation index superposed to one year by using the improved logistic curve to obtain a quaternary phase change curve of the forest canopy spectrum.
Although the present invention integrates the data over years to reduce the effects of the annual effects, the analysis using the data over years takes into account the effects of the annual effects. In another embodiment, annual changes are determined by fitting each year data after curve fitting using the years data by introducing a coefficient of variation in the equation.
This method assumes that the fitted curve is applicable for each year, with only the time of onset varying between each year. Therefore, data of different years can be differentiated, the influence of the annual effect on the vegetation growth initial period can be obtained through the variation coefficient, the variation trend of the initial date can be obtained through linear fitting analysis, and the formula is as follows:
in the above formula, b1A coefficient of variation in the rise period, b2Is the coefficient of variation during the descent.
The invention is further verified by the following specific experimental data.
According to the method, the fitted primary and secondary forest NDVI season phase change curve equations of the broad-leaved red pine are respectively as follows:
there are many ways to define the time of the start and end of the growing season of vegetation from curves, and it is most common to use half the difference between the maximum and minimum NDVI values as the point at which vegetation starts and stops growing, since it is the point at which the logistic curve changes most rapidly, with the first derivative value being the largest. While the date when NDVI begins to increase and decrease may fluctuate slightly with environmental changes, it is relatively stable at 1/2, which is most representative of the average conditions in a stand. As shown in FIGS. 2-3, according to this method, the growth period of the primary and secondary broadleaf mixed forests in Changbai mountain is about 120 days (4 months and 30 days), the growth stop period is about 270 days (9 months and 27 days), and the growth season length is about 150 days.
In the prior art, SPOT-VGT data (resolution: 1km) are used for researching vegetation phenology in Changbai mountain areas, and the result is that the forest land growth season of the Changbai mountain areas starts from about 100-. The result of using the dynamic threshold method with MODIS data (resolution 500m) is that the tree begins to spread leaves in late 4 th days, and stops growing around 280-290 days, and the length of the growing season is about 180 days. Therefore, the method is basically consistent with the current research result, and can be used for researching forest stand scale spectrum quaternary change.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A forest canopy spectrum quaternary change monitoring method based on medium-resolution images is characterized by comprising the following steps:
dividing the same forest stand into a plurality of sub-regions with the same size, and randomly selecting a certain number of the sub-regions as sample regions;
acquiring remote sensing image data of the sample area for years as sample data;
preprocessing the sample data;
extracting spectral information in the preprocessed sample data, and respectively calculating the normalized vegetation index of each image shooting date of the forest canopy in multiple years based on the spectral information;
overlapping the normalized vegetation indexes of all image shooting dates in the forest canopy for a plurality of years to one year;
introducing a logistic curve and improving the logistic curve;
and fitting the normalized vegetation index superposed to one year by using the improved logistic curve to obtain a seasonal variation curve of the forest canopy spectrum.
2. The method for monitoring spectral quaternary changes of the canopy of a forest according to claim 1, wherein the remote sensing image data is a medium resolution image obtained based on a Landsat satellite, and the spatial resolution of the image is 30 m.
3. The method as claimed in claim 1, wherein the Google Earth Engine is used to extract and preprocess the sample data.
4. The method for monitoring the spectral quaternary changes of the forest canopy according to claim 1, wherein the preprocessing the sample data comprises:
selecting a remote sensing image with the best imaging quality in the same period in the sample data;
carrying out cloud layer detection on all the selected remote sensing images, and removing the remote sensing images covered by clouds and cloud images;
and cutting the remote sensing image subjected to cloud layer detection.
5. The method as claimed in claim 1, wherein the spectral information is reflectance values of red, green, blue and near infrared bands.
6. The method for monitoring spectral quaternary changes of forest canopy according to claim 5, wherein the normalized vegetation index is calculated by the following formula:
in the above formula, ρNIRRepresenting the reflectivity, p, of the near infrared bandREDIndicating the reflectivity in the red band.
7. The method for monitoring spectral quaternary changes of forest canopy based on medium resolution image as claimed in claim 1, wherein introducing and improving logistic curve comprises:
introducing the logistic growth curve, wherein the expression is as follows:
in the above formula, m is a slope, n is an offset, t is time, and y is spectral information;
the logistic growth curve is preliminarily improved to be suitable for describing blades with different shapes in forest canopies, and the expression is as follows:
in the above formula, VminBackground value, V, for spectral informationampIs a variation range;
introducing a logistic descending curve, and fitting the logistic descending curve with the logistic increasing curve to generate a final logistic curve equation, wherein the expression of the logistic descending curve is as follows:
in the above formula, m1For the slope of the rise period, m2For the falling phase slope, n1For the offset of the rise period, n2Is the decline period offset.
8. The method for monitoring spectral quaternary changes in forest canopy based on medium resolution imaging as claimed in claim 7, wherein logistic curves are introduced and modified, further comprising:
introducing a variation coefficient, fitting by using remote sensing image data for many years, and obtaining the influence of an annual effect on the growth initial period of forest vegetation, wherein the expression is as follows:
in the above formula, b1A coefficient of variation in the rise period, b2Is the coefficient of variation during the descent.
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